Search Results (754)

In today’s service-driven economy, brands increasingly turn to social media marketing activities (SMMA) to build meaningful, long-term customer relationships. Grounded in social exchange theory (SET), this study examines how SMMA influences customer engagement (CE), relational benefits (confidence, social, and special treatment), and ultimately relationship equity—offering new insights for trust-based services, such as banking. SET provides a powerful lens to explain how perceived value in brand-initiated exchanges drives customer reciprocity. A between-subjects experimental design (n = 298) was employed using real social media ads from a bank to enhance ecological validity. Participants were randomly assigned to ad exposure or control conditions, and data were analyzed using PLS-SEM. Results show that SMMA significantly enhances CE and relational benefits. In turn, CE, along with confidence and social benefits, contributes to relationship equity. Special treatment benefits, however, had no significant effect. Ad exposure amplified the impact of SMMA on CE and relationship outcomes. Theoretically, this study advances SET by revealing how digital brand interactions translate into lasting customer bonds. Practically, the findings indicate that banks should prioritize SMMA for increased CE and relational benefits. When combined with targeted advertising efforts, this can significantly improve relationship equity. Full article

This study examines the impacts of Enterprise Resource Planning (ERP) systems on financing costs from the dual perspectives of risk management and relative value creation based on corporate value maximization objectives. Data were manually collected from the listed companies in China. It is found that the equity financing cost and debt financing cost of enterprises implementing ERP systems are both significantly higher than those without, and the impact of the ERP systems on equity financing cost is more significant than on debt financing cost. The endogeneity problems are addressed using the fixed effect, the instrumental variables in the two-stage least squares (2SLS) regression test, and the Heckman two-stage regression test. Further exploration into the underlying reasons for these results through mechanism analysis reveals that ERP systems can systematically and effectively enhance risk management levels and corporate value returns, bringing higher returns for investors and achieving a win-win situation. These research findings fundamentally help alleviate the agency problems between companies and investors, and also explain the advantages of an investment-oriented capital market in resolving conflicts among its various participants. Additionally, heterogeneity analysis further shows that the ownership structure and age structure of enterprises have a significantly negative moderating effect on the above results, and the moderating effect on equity financing cost is stronger than on debt financing cost. Full article

This study examines the dynamic and nonlinear effects of global interest rate (based on the G7 market) shocks on equity markets in BRICS countries. A World Interest Rate (WIR) index is constructed using principal component analysis of short-term interest rates from developed economies. The analysis applies Distributed Lag Nonlinear Models (DLNMs) to evaluate the temporal response of each market to positive and negative WIR shocks over a six-period horizon. The results reveal notable asymmetries and heterogeneity. Brazil and Russia experience stronger reactions to negative shocks, while India and China show milder or delayed effects. South Africa stands out for its persistent and symmetric sensitivity to both types of shocks, suggesting deeper exposure to global financial cycles. The DLNM framework allows for a nuanced interpretation of exposure-lag relationships, offering new insights into how global monetary conditions affect emerging markets. These findings highlight that financial integration does not imply uniform vulnerability across countries and that global liquidity shocks can trigger diverse equity market responses. This paper contributes to the literature on international financial linkages and provides relevant implications for investors and policymakers managing portfolio exposure or economic risk in emerging markets. Full article

Stock price forecasting remains a central challenge in financial modeling due to the non-stationarity, noise, and high dimensionality of market dynamics, as well as the growing importance of unstructured textual information. In this work, we propose a multimodal prediction framework that combines FinBERT-based financial sentiment extraction with technical and statistical indicators to forecast short-term stock price movement. Contextual sentiment signals are derived from financial news headlines using FinBERT, a domain-specific transformer model fine-tuned on annotated financial text. These signals are aggregated and fused with price- and volatility-based features, forming the input to a gradient-boosted decision tree classifier (XGBoost). To ensure interpretability, we employ SHAP (SHapley Additive exPlanations), which decomposes each prediction into additive feature attributions while satisfying game-theoretic fairness axioms. In addition, we integrate differential privacy into the training pipeline to ensure robustness against membership inference attacks and protect proprietary or client-sensitive data. Empirical evaluations across multiple S&P 500 equities from 2018–2023 demonstrate that our FinBERT-enhanced model consistently outperforms both technical-only and lexicon-based sentiment baselines in terms of AUC, F1-score, and simulated trading profitability. SHAP analysis confirms that FinBERT-derived features rank among the most influential predictors. Our findings highlight the complementary value of domain-specific NLP and privacy-preserving machine learning in financial forecasting, offering a principled, interpretable, and deployable solution for real-world quantitative finance applications. Full article

Natural resource assets inherently integrate tripartite synthesis of legal, economic, and ecological attributes. They serve dual critical functions as foundational elements supporting the evolution of new-quality productive forces and pivotal mechanisms safeguarding ecosystemic integrity. It has become a global consensus and direction of action to advance comprehensive supervision of natural resource assets and practice the concept of “Community of Life for Human and Nature”. Under the background of the super-ministry system restructuring in China, comprehensive supervision of natural resource assets remains challenged by system fragmentation in supervision objectives and multifaceted interest conflicts among stakeholders. In light of this, this research focuses on the theoretical justification and system optimization of the comprehensive supervision of natural resource assets in China. Using comparative analysis and normative analysis methods, we validate the system’s function on the comprehensive supervision of natural resource assets, summarize foreign experiences, and ultimately aim to explore the optimization pathway of the legal system for the comprehensive supervision of natural resource assets. The results show the following: (1) The choice of the legal system for the comprehensive supervision of natural resource assets emerges as the functional product aligning societal objectives, the rational paradigm for achieving efficient resource allocation, and the adaptive response to the external effects of common property. (2) The system supply of comprehensive supervision of natural resource assets in foreign countries is characterized by normative convergence in conceptual elements and typological categorization in objectives and objects. Therefore, this research recommends that, in order to optimize the system of the comprehensive supervision of natural resource assets in China, (1) in terms of protection of source, natural resource assets should be categorized, with operational natural resource assets focusing on management and public welfare natural resource assets focusing on conservation. (2) In terms of valuation, the economic valuation of natural resource assets should be integrated with ecosystem service assessments to enhance fair market equity. (3) In terms of method, the big data center should be established to enable the synergistic integration of technological innovation and system reforms. (4) In terms of subject, requiring the participation of various government departments, non-governmental organizations, the general public, and other parties could realize the connection of different legal bases for the comprehensive supervision of natural resource assets and the balance of multiple rights and interests, which should help to achieve balanced resource efficiency and biodiversity conservation and safeguard national ecological security. Full article

Customers’ repurchase intention is a key driver of sustained profitability for companies. However, the influencing factors of customers’ repurchase intention and their direct mechanisms of action are not yet clear. This study empirically examines the relationships among intrinsic quality, perceptual quality, brand equity, customer satisfaction, online word-of-mouth, and customers’ repurchase intention using a structural equation model. A customer repurchase intention model is constructed with customer satisfaction as the mediating variable and online word-of-mouth as the moderating variable. The empirical results show that intrinsic quality, perceptual quality, and brand equity have a significant impact on customer satisfaction. Notably, intrinsic quality exerts an indirect effect on customers’ repurchase intention primarily through customer satisfaction, with no significant direct impact, while perceptual quality and brand equity have significant direct effects on repurchase intention. Customer satisfaction plays a partial mediating role between intrinsic quality, perceptual quality, brand equity, and repurchase intention. Online word-of-mouth has a significant moderating effect on the relationship between customer satisfaction and customers’ repurchase intention. These findings provide actionable insights for smartphone enterprises to optimize product development, brand management, and online reputation strategies, thereby enhancing customer loyalty and market competitiveness. Full article

The US has undertaken both unconventional and conventional monetary policy stances in response to the COVID-19 pandemic and the Ukraine–Russia conflict, and there has been much debate on the effects of these various monetary policies on global financial markets. Our study considers the debate in the context of South Africa and uses the quantile–frequency connectedness approach to examine static and dynamic systemic spillover between the US shadow short rate (SSR) and South African equity, bond and currency markets between 1 December 2019 and 2 March 2023. The findings from the static analysis reveal that systemic connectedness is concentrated at their tail-end quantile distributions and US monetary policy plays a dominant role in transmitting these systemic shocks, albeit these shocks are mainly high frequency with very short cycles. However, the dynamic estimates further reveal that US monetary policy exerts longer-lasting spillover shocks to South African financial markets during periods corresponding to FOMC announcements of quantitative ‘easing’ or ‘tapering’ policies. Overall, these findings are useful for evaluating the effectiveness of the Reserve Bank’s macroprudential policies in ensuring market efficiency, as well as for enhancing investor decisions, portfolio allocation and risk management. Full article

This paper investigates the macro-financial consequences of recent monetary policy tightening cycles, focusing on the distinction between soft and hard landings. Using an OLS regression framework applied to U.S. and Euro Area data from 1994 to 2023, we analyze the response of equity and bond markets, inflation, and GDP growth to central bank interest rate hikes. The findings suggest that, in most past tightening episodes, central banks succeeded in engineering soft landings without severe disruptions to market conditions or economic growth. However, the current post-pandemic context may lead to a two-stage adjustment, as inflation persistence and geopolitical shocks alter standard transmission dynamics. The study contributes to the ongoing policy debate on the timing and intensity of rate hikes, offering historical insights and empirical evidence from capital market signals. Full article

The integration of artificial intelligence (AI) into environmental, social, and governance (ESG) disclosure represents a critical frontier for corporate transparency in emerging markets. This study investigates the relationship between AI adoption in ESG reporting, disclosure quality, and financial performance among 180 Saudi-listed companies (2021–2024) within Vision 2030’s transformative context. Using the System Generalized Method of Moments (GMM) estimation with panel unit root and cointegration testing to ensure stationarity assumptions and addressing endogeneity through bounding analysis, the study finds that AI adoption intensity significantly enhances ESG disclosure quality (β = 0.289, p < 0.001), with coefficient significance assessed through t-tests using firm-clustered robust standard errors. Enhanced disclosure quality translates into meaningful financial performance improvements: 0.094 percentage points in return on assets (ROA), 0.156 in return on equity (ROE), and 0.0073 units in Tobin’s Q. Mediation analysis reveals that 73% of AI’s total effect operates through improved ESG quality rather than direct operational benefits. The findings demonstrate parametric bounds robust to macroeconomic confounders, suggesting AI-enhanced transparency creates substantial shareholder value through strengthened stakeholder relationships and reduced information asymmetries. Full article

Against the backdrop of pervasive land fragmentation and high transaction costs, organized land transfer has emerged as a growing trend in China’s agricultural land market, facilitating the transition toward moderate-scale farming. Based on survey data from 1472 households across 72 villages in Jiangsu Province, this study investigates the impact of organized land transfer on agricultural land allocation efficiency and explores the underlying mechanisms. The results show that organized land transfer significantly enhances agricultural land allocation efficiency. This finding proves to be robust across a series of robustness analyses. Specifically, organized land transfer enhances land allocation efficiency, primarily by enhancing transfer stability, expanding the transfer scale, and broadening the transfer scope. Moreover, our analysis of moderating factors reveals that the strength of the village collective economy positively moderates the relationship between organized land transfer and efficiency, whereas lineage networks exert a negative moderating influence. Addressing equity implications, this study also examines the model’s impact on farmers’ autonomy. The findings indicate that organized land transfer significantly suppresses transfer willingness, particularly in those with low incomes and the elderly. These results carry significant policy implications: when promoting organized land transfer, it is crucial to balance the strengthening of village collectives’ intermediary role with robust regulatory frameworks designed to safeguard farmers’ land rights and autonomy. Full article

In the paper, using the three-statement financial modelling methodology as applied to a representative development project, we aim to analyse, ex ante, the industry-level impact of transition to mandatory escrow schemes in residential and mixed-use construction in Tashkent city (due to be implemented in Uzbekistan from 2026). Modelling single-milestone escrow plans against the current steep-discount advance-based system of off-plans as a baseline, the model accounts for salient institutional features of the Tashkent city development market, including land auctioning, full-cycle Value-added tax (VAT) accounting, and Tax loss carryforward provisions. It also incorporates a framework for demand-driven residual valuations for the development land element. Our findings indicate practically unchanged cashflow profitability of developers on the market in question. Around 30% p.a. in nominal Free-cashflow-to-equity based IRRs expressed in the national currency, provided that the transition to the greater use of leverage in funding unfolds as expected. The disappearance of steep off-plan discounts while the transition to escrows unfolds will be countervailed by the reliance on costly loans from escrow banks. Absent the greater use of leverage, the IRR (FCFE) profitability of the developers is expected to decline by some 5%. For the apartment buyers, this is effectively equivalent to increasing property transaction prices on the primary market in line with their headline asking amounts. Thus-generated economic surplus will be partially captured by the developers and partially passed through to escrow banks, increasing their gross profits by up to $50M, p.a. due to their new role in financing Tashkent city residential developments that are still largely equity-driven. Apart from this effect, we find only a moderate financial leverage influence on developers’ profitability due to the high-interest-rate environment prevailing in Uzbekistan. We also find a demand-driven pressure on land auction prices suggested by increasingly back-loaded alterations in project cashflow profiles. This study also purports to make a material contribution to the evolving body of literature on financial modelling of apartment and mixed-use property developments by offering a flexible three-statement modelling framework with innovative endogenised equity management features. Full article

This study evaluates the performance of U.S. data center Exchange-Traded Funds (ETFs) relative to major equity and technology benchmarks, using monthly returns from January 2000 through December 2024, with particular emphasis on the COVID-19 period and the subsequent post-vaccine era. Data center ETFs have not provided better risk-adjusted returns even though they are often advertised as access points to the digital economy. Digital infrastructure demand increased through the pandemic but did not improve the performance of these funds which stayed weak across both traditional and conditional multi-factor asset pricing models. These ETFs struggle with asset selection and market timing proficiency, which leads to relatively poor performance results during volatile market conditions. The downside risks linked to these funds tend to match or exceed the downside risks of broader indices like the S&P 1500 Information Technology Index. Although these investments are based on strong thematic narratives, they do not achieve returns that align with investor expectations. Full article

Economic systems face critical challenges, including widening income inequality, unemployment driven by automation, mounting public debt, and environmental degradation. This study introduces Economocracy as a transformative framework aimed at addressing these systemic issues by integrating democratic principles into economic decision-making to achieve social equity, economic efficiency, and environmental sustainability. The research focuses on two core mechanisms: Economic Productive Resets (EPRs) and Economic Periodic Injections (EPIs). EPRs facilitate proportional redistribution of resources to reduce income disparities, while EPIs target investments to stimulate job creation, mitigate automion-related job displacement, and support sustainable development. The study employs a theoretical and analytical methodology, developing mathematical models to quantify the impact of EPRs and EPIs on key economic indicators, including the Gini coefficient for inequality, unemployment rates, average wages, and job displacement due to automation. Hypothetical scenarios simulate baseline conditions, EPR implementation, and the combined application of EPRs and EPIs. The methodology is threefold: (1) a mathematical–theoretical validation of the Cycle of Money framework, establishing internal consistency; (2) an econometric analysis using global historical data (2000–2023) to evaluate the correlation between GNI per capita, Gini coefficient, and average wages; and (3) scenario simulations and Difference-in-Differences (DiD) estimates to test the systemic impact of implementing EPR/EPI policies on inequality and labor outcomes. The models are further strengthened through tools such as OLS regression, and Impulse results to assess causality and dynamic interactions. Empirical results confirm that EPR/EPI can substantially reduce income inequality and unemployment, while increasing wage levels, findings supported by both the theoretical architecture and data-driven outcomes. Results demonstrate that Economocracy can significantly lower income inequality, reduce unemployment, increase wages, and mitigate automation’s effects on the labor market. These findings highlight Economocracy’s potential as a viable alternative to traditional economic systems, offering a sustainable pathway that harmonizes growth, social justice, and environmental stewardship in the global economy. Economocracy demonstrates potential to reduce debt per capita by increasing the efficiency of public resource allocation and enhancing average income levels. As EPIs stimulate employment and productivity while EPRs moderate inequality, the resulting economic growth expands the tax base and alleviates fiscal pressures. These dynamics lead to lower per capita debt burdens over time. The analysis is situated within the broader discourse of institutional economics to demonstrate that Economocracy is not merely a policy correction but a new economic system akin to democracy in political life. Full article

This study investigates the predictive performance of financial indicators in forecasting stock prices within the automotive sector using an adaptive neuro-fuzzy inference system (ANFIS). In light of the growing complexity of global financial markets and the increasing demand for automated, data-driven forecasting models, this research aims to identify those financial ratios that most accurately reflect price dynamics in this specific industry. The model incorporates four widely used financial indicators, return on assets (ROA), return on equity (ROE), earnings per share (EPS), and profit margin (PM), as inputs. The analysis is based on real financial and market data from automotive companies, and model performance was assessed using RMSE, nRMSE, and confidence intervals. The results indicate that the full model, including all four indicators, achieved the highest accuracy and prediction stability, while the exclusion of ROA or ROE significantly deteriorated model performance. These findings challenge the weak-form efficiency hypothesis and underscore the relevance of firm-level fundamentals in stock price formation. This study’s sector-specific approach highlights the importance of tailoring predictive models to industry characteristics, offering implications for both financial modeling and investment strategies. Future research directions include expanding the indicator set, increasing the sample size, and testing the model across additional industry domains. Full article

This paper explores the intervening role in SEP performance of corporate environmental, cultural, and ethnic activities (ECEAs) and diversity, equity, inclusion, and social initiatives (DEISIs) on blockchain adoption (BCA) strategy, particularly useful in the Western Balkans (WB), which demands transparency due to extended fraud and ethnic complexities. In this domain, a question has been raised: In BCA strategies, is there any correlation between SEP performance and ECEAs and DEISIs in a mediating role? A serial mediation model was tested on a dataset of 630 WB and EU companies, and the research conceptual model was validated by CFA (Confirmation Factor Analysis), and the SEM (Structural Equation Model) fit was assessed. We found a statistically sound (significant, positive) correlation between BCA and ESG success performance, especially in the innovation and integrity ESG performance success indicators, when DEISIs mediate. The findings confirmed the influence of technology, and environmental, cultural, ethnic, and social factors on BCA strategy. The findings revealed some important issues of BCA that are of worth to WB companies’ managers to address BCA for better performance. This study adds to the literature on corporate blockchain transformation, especially for organizations seeking investment opportunities in new international markets to diversify their assets and skill pool. Furthermore, it contributes to a deeper understanding of how DEI initiatives impact the correlation between business transformation and socioeconomic performance, which is referred to as the “social impact”. Full article